Conventional electronic gaming machines work in the following manner. Each game has a set of rules, known as a combination, and a set of winning and non-winning outcomes. A player makes a wager and starts the game and the machine determines the result of the bet. This determination involves a random number generator (RNG) being used to select one of the winning or non-winning outcomes, of which there could be millions of possibilities, and the game then displaying this outcome in some fashion.
An example of the above would be a video spinning reel game. Once the player presses a “Bet” button, the RNG is used to select a “stopping position” for each of the reels. These are the final positions of the spinning reels, to be displayed at the end of the game. The game software does not immediately display these stopping positions. Rather, it first starts the reels spinning from the previous stopping positions and continues to spin the reels until each of the RNG derived stopping positions comes into view on screen. As each stopping position reaches the correct point on screen, the reels are stopped. Thus, the player gets the impression that the derivation of the final position of each reel was done at the end of the reel spin rather than at the beginning.
The reason why game outcomes should be known before the game cycle finishes is that, in the event of a failure of a machine during the game cycle, it is important that the machine does not alter its behaviour. By predetermining and storing the outcome in non-volatile memory, the machine can ensure that if power is lost during a game cycle then, upon resumption of power, the game can continue to the same conclusion.
Another approach to a predetermined outcome would be to model a physical system using software within the gaming machine. In the example of a keno or bingo game, the previous predetermined method would be for the software to select a ball which is going to be produced from the on-screen cage, and then display an animation of this outcome occurring. The problem with this method is that, unless a large number of animations are stored, it quickly becomes apparent that this is not an accurate simulation of a keno game. Modelling the physical system using software would mean, in the case of keno, starting all of the 40 or so virtual “balls” at random positions within a virtual “cage”. The simulation software would then simulate the effects of gravity, collisions and all the other forces which would cause the balls to move around randomly within the cage. As the simulation progressed, it would be represented on the gaming machine's screen. At the end of the simulation one of the balls would be “picked” by the simulation which displays a graphical representation of a mechanical arm picking the ball in an analogous way to the way conventional physical lottery machines work.
This approach has some advantages. It would produce a much more realistic looking display of a keno game and would appear to the player to be far more random. Unfortunately, this very randomness would also make it far more difficult for the game software to accurately know which ball is going to be selected, since selection takes place at the end, not the beginning, of the simulation. It would therefore be up to the gaming machine manufacturer to try and prove that the physical system being modelled was sufficiently random in its outcome and, more importantly, free of bias.
Modelling physical processes is relatively straightforward, but the interactions are such that, although it is easy to model from a starting position to derive an ending position—so it is easy to model a ball being dropped on a roulette wheel and then run the simulation through till the ball stops and see where it stops, it is much more difficult to start with an end position and try to derive the starting position that lead to the end position. In the case of roulette this would be akin to trying to derive a starting position, velocity and acceleration for a roulette ball in the croupier's hand from an end position that was at rest and in one of the numbered slots on the roulette wheel. Clearly, this would be an exceptionally difficult task.
Also, roulette is a good example of the problem of bias. Casinos must guard against any charge of bias by using roulette wheels that are made to the highest quality and checked on a regular basis. Any attempt at accurately modelling a roulette wheel system using a computer would also suffer from this need to ensure that the software would not tend to favour any of the possible outcomes for the game. Since gaming machines are heavily regulated, it would be necessary for a gaming machine manufacturer using a physical simulation game to prove that no bias was present to regulators before such a machine could be authorised for sale. Given the complexity of any non-trivial physical simulation, this proof would be hard to furnish.